• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.742-748
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• 2009

Most commercial digital still cameras use a single sensor array (e.g., CMOS or CCD) with color filter array (CFA) to reduce the cost and size. Since the image obtained with CFA has only one color value per pixel, the demosaicing is needed to acquire missing two color values. Although many demosaicing methods have been proposed, they still have artifacts such as rainbow and zippering artifact. In this paper, we propose the simple demosaicing algorithm using tendency of neighbor pixels with the enhanced weighting function. In the experimental results, our algorithm shows much better subjective qualities of the images than conventional demosaicing algorithm and improves objective qualities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2555-2562
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• 2010

In this paper, we propose the method that extracts sea level measurements graph from the sea level measurements recording paper image with a little interaction. At first, a pixel that is included in the graph area is selected. Then, background pixels are automatically determined using the distance between a selected pixel and other pixels on LAB color space. In each vertical line, a pixel that is the nearest to the selected pixel on LAB color space is extracted and the graph area is determined using that pixels. Experimental results show that the sea level measurements graph can be extracted with a few interaction from the various sea level measurements recording paper images.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.1-12
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• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.2
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• pp.25-31
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• 2009

The image processing methods are widely used in many industrial fields to detect defections in inspection devices. In this study an image processing method was conducted for the detection of abnormal pixels in a OLED(Organic Light Emitting Diode) type panel which is used for small size displays. The display quality of an OLED device is dependent on the pixel formation quality. So, among the so many pixels, to find out the faulty pixels is very important task in manufacturing processing or inspection division. We used a line scanning type BW(Black & White) camera which has very high resolution characteristics to acquire an image of display pixel patterns. And the various faulty cases in pixel abnormal patterns are considered to detect abnormal pixels. From the results of the research, the normal BW pixel image could be restored to its original color pixel.

• ETRI Journal
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• v.32 no.6
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.242-245
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• 2000

In this paper, we present a color interpolation technique based on artificial neural networks for a single-chip CCD (charge-coupled device) camera with a Bayer color filter array (CFA). Single-chip digital cameras use a color filter array and an interpolation method in order to regenerate high quality color images from sparsely sampled images. We applied 3-layer feedforward neural networks in order to interpolate missing pixel from surrounding pixels. And we compared the proposed method with conventional interpolation methods such as the proposed interpolation algorithm based on neural networks provides a better performance than the conventional interpolation algorithms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.232-237
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• 2013

This paper presents image database retrieval based on maximum color occurrenceusing Hue, Saturation and Value (HSV) color space. Our system is based on color segmentation. We dividedthe image into n number of areas based on different selected ranges of hue and value, then each area is partitioned into m number of segments based on the number of pixels it contains, after this we calculated the maximumcolor occurrence in each segment and used its HSV value. This is used as a feature vector.

• Journal of the Korean Society of Visualization
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• v.6 no.2
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• pp.20-27
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• 2008

The color sorting technique utilizing the image processing method is very applicable tool to analyze motion of a free-falling object in many agricultural and industrial research fields. In the present study, we have developed an image processing system and algorithm to sort good quality rice grains effectively from the bad ones. The system employs a high speed rate line-scan CCD camera with 2K-pixels and worked with a high speed DSP and FPGA in-line. It can accumulate acquired line-scan image data and visualize each grain image clearly. As a result, we can easily calculate the number of pixels occupied by grain(=grain size), gray level and its correct position by visualizing grain images rapidly.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.6
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• pp.649-656
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• 2014

In this paper, we address the problem of center lane detection algorithm for autonomous driving. Color information for center lane is gathered by analyzing a row line color distribution of road in front of a vehicle. The candidate pixels for center lane are extracted from the histogram of road colors. Morphological filtering and clustering process are applied to the candidate pixels to extract the exact center lane. We predict a expected area of center lane and search only the regions in subsequent frames, that reduces the time required for center lane detection.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.116-119
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• 2018

In this paper, we propose a new single-image dehazing method. The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry. The transmission values generated by the proposed method maximize the contrast of dehazed pixels, while preventing over-saturated pixels. The values are also statistically robust because they are calculated from the averages of the haze pixel values. Furthermore, rather than apply a highly complex refinement process to reduce halo or unnatural artifacts, we embed a fuzzy segmentation process into the construction of the color ellipsoid so that the proposed method simultaneously executes the transmission calculation and the refinement process. The results of an experimental performance evaluation verify that compared to prevailing dehazing methods the proposed method performs effectively across a wide range of haze and noise levels without causing any visible artifacts. Moreover, the relatively low complexity of the proposed method will facilitate its real-time applications.